In order to determine the condition of machinery, it is normal to monitor and analyse a series of measurable “condition indicators” which themselves reflect aspects of the condition of the machine. This allows machine deterioration and/or problems to be detected and, if necessary, addressed at an early stage.
However, particularly with complex machinery such as gas turbine engines, the number of indicators that must be monitored to obtain an overall picture of the engine's condition can be high. This in turn means that the task of analysing the complete series of indicators to determine the condition of the engine can be a complex one.
Taking again the example of a gas turbine, it is known to collect performance and vibration data from the engine over time to be analysed off-line by one or more experts. Typically the performance data will be compared with simulated data for the same engine and, based on this comparison, an expert will form a view as to the health of the engine. Additionally, a small amount of vibration data will be reviewed, giving a superficial view of gross changes in engine behaviour. If a problem is detected, the vibration data may then be analysed in more detail, often by another expert, to look for any abnormal indications which might be symptomatic of underlying mechanical problems which could lead to a loss of performance and operability.
In WO 02/03041 (which is hereby incorporated by reference) we describe a system for monitoring complex machinery which incorporates a learnt model of normal behaviour and which can register abnormal events in real time.
Bearings are often critical parts of machinery and hence bearing performance can contribute to machinery condition. Defects in bearing surfaces can affect machine functioning and catastrophic bearing failure can even compromise safety. For example, the proper performance of the bearings which support and locate the rotating shafts of a multi-shaft gas turbine engine is integral to engine operation.
Machines with rotating components such as gas turbine engines are subject to vibratory forces at frequencies which are related to the angular velocity of the respective component. These frequencies are conventionally known as engine order forcing frequencies, each engine order corresponding to a rotational frequency of a particular component (or harmonic thereof) and exerting a corresponding vibratory force on the machinery.
The forces may arise because e.g. the machinery is out of balance on a particular shaft, stiffness irregularities in the machine components, and, in the case of a gas turbine engine, aerodynamic interactions between the engine blades. At a given speed of rotation, a number of these engine orders are generally activ and result in corresponding measurable vibration responses. A “tracked order” is a specific vibration response which is associated with a respective machine component. Tracked orders can be illustrated by plotting the frequency of the particular response against engine speed or time.
Conventional methods of bearing anomaly detection involve monitoring the energy of a vibration frequency that is known to be indicative of bearing defect, i.e. monitoring for the appearance of a specific tracked order. If energy exists at the frequency the bearing is faulted.